What are the challenges of conventional supply chain forecasting?

As the traditional supply chain comes up with certain constraints, it requires urgent strategic revamping to accomplish the latest consumer demand trends and effectively manage the ever-increasing supply chain challenges. These concerns within the supply chain network are primarily driven by a sequence of consumer expectations, international convolutions, abundant routes to the market, and other miscellaneous factors. Hence, supply chain administrators need to create smart business strategies to nullify the challenges to keep everything flowing smoothly. Given the ongoing crisis, tech-enabled supply chain forecasting and demand sensing platforms are the best bet for organizations across all industries and sectors.

Supply chain challenges will likely persist in the coming days. Usually, these challenges are propelled by higher consumer spending, increased awareness of the convenience of online purchases, and catch-up needed from last year’s blockages. It has now become imperative for organizations to implement efficient supply chain forecasting tools for connected and seamless operation of the demand and supply business networks.

Significance of supply chain forecasting

Supply chain forecasting predicts a business’s customers’ demand, supply, and expenses. It primarily comprises exploring the competition, accumulating, compiling supplier data, and ultimately evaluating past patterns to predict the future of an industry. Hence, this justifies that forecasting is a significant skill for a supply chain supervisor as it embodies numerous skills one should attain and develop.

Enterprises aim at making the best decision about their future demand and supply. Whether it is stock inventory, cargo booking, budget planning, or expanding to new markets, a proper pre-defined plan can ensure the smooth outflow of goods and cash inflow. Needless to say, forecasting is the starting point. But AI-powered supply chain forecasting tools determine future events based on market statistics, where individual opinion and bias have no say, as is the case with traditional methods. Hence, there is little room for errors.

Also, market disruptions open new doors for growth. A move towards online retail, a rise in consumer confidence, curbed demand, and accumulated savings can all spring immense opportunities for online brands. In order to stay ahead of changing times, predictive analytics with technology plays a defining role in forecasting changes before they occur. Hence, businesses gain an edge over the competition and stay ahead in the rat race within the e-commerce fraternity. Sadly, conventional methods fall short of doing just that – as proven by 43% unused inventory compared to the total product sold in a year.

This emphasizes the need for AI-powered harmonized data to handle multiple types of internal and external data sets to enhance forecast accuracy.

Challenges of conventional supply chain forecasting

Siloed data: One of the most significant challenges for supply chain forecasting in recent times has been the siloed data systems with inconsistent collaboration and visibility for others, leading to issues such as:

Additionally, globalization has increased the data integration problems in modern-day supply chains. Latest business simulations with outsourced manufacturing, partnerships and acquisitions have blurred the divisional boundaries. Consequently, with data playing an important role in such a landscape, silos data puts the business at a bigger risk. As a result, it becomes difficult to give demand forecasts that can lead to inaccurate supply chain forecasting.

Fragmented demand forecasting: The pandemic has made the supply chain more complex by smashing the currently followed demand forecasting approach of many suppliers and retailers, thus putting them in a situation where they are not sure of the inventory stock or manufacture at a given time. Hence, companies are developing strategies to improve customer demand forecasting by using a new data-driven in real-time for more accuracy.

Shifting consumer attitudes and changing demand patterns: Consumer attitudes and behaviors have been shifting in some big ways during the pandemic, like lowering the threshold for delivery times and raising the requirements for a positive customer experience. For this to happen, a quick responsive supply chain can utilize the power of some of the best supply chain forecasting tools to optimize delivery execution and handle augmented demand comfortably.

Key benefits of supply chain forecasting:

Conclusion

It cannot be denied that amid the pandemic crisis and supply-demand volatilities, companies are better prepared now against future shocks. Thus, they are implementing the latest predictive analysis and supply chain management (SCM) optimization.

Enterprises need agility to stay afloat in uncertain times. And agility is a trait that can be acquired with real-time insights. Intelligent software solutions for supply chain forecasting capture granular data from the network and help businesses to stay agile and prepared with decision intelligence. Hence, it is increasingly imperative for modern-day supply chains to embrace a holistic perspective with artificial intelligence and analytics.